Evidence map›Paper›PMID 37292994›Full record

ArticlebioRxiv : the preprint server for biology2023

scGHOST: Identifying single-cell 3D genome subcompartments.

Kyle Xiong, Ruochi Zhang, Jian Ma

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Kyle XiongComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Ruochi ZhangComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Jian MaComputational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.

Funding

Multiscale Analyses of 4D Nucleome Structure and Function by Comprehensive Multimodal Data IntegrationUM1HG011593 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI ALBER, FRANK, BELMONT, ANDREW STEVEN · 2020 to 2024
$10.4M
Computational Methods for Next-Generation Comparative GenomicsR01HG007352 · NHGRI · UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN · PI MA, JIAN · 2014 to 2023
$2.8M
Computational methods for studying single-cell 3D genomeR01HG012303 · NHGRI · CARNEGIE-MELLON UNIVERSITY · PI DUAN, ZHIJUN, MA, JIAN · 2022 to 2025
$2.2M
Spatial omics technologies to map the senescent cell microenvironmentUG3CA268202 · NCI · BROWN UNIVERSITY · PI MA, JIAN, NERETTI, NICOLA · 2021 to 2022
$916k
Single-cell multiomic methods for studying genome structure and functionR56HG013092 · NHGRI · UNIVERSITY OF WASHINGTON · PI DUAN, ZHIJUN, MA, JIAN · 2023 to 2023
$400k
NCI NIH HHS UG3 CA268202NHGRI NIH HHS R01 HG007352NHGRI NIH HHS R01 HG012303NHGRI NIH HHS R56 HG013092NHGRI NIH HHS UM1 HG011593
6 · The paper itself

Abstract

New single-cell Hi-C (scHi-C) technologies enable probing of the genome-wide cell-to-cell variability in 3D genome organization from individual cells. Several computational methods have been developed to reveal single-cell 3D genome features based on scHi-C data, including A/B compartments, topologically-associating domains, and chromatin loops. However, no scHi-C analysis method currently exists for annotating single-cell subcompartments, which are crucial for providing a more refined view of large-scale chromosome spatial localization in single cells. Here, we present scGhost, a single-cell subcompartment annotation method based on graph embedding with constrained random walk sampling. Applications of scGhost to scHi-C data and single-cell 3D genome imaging data demonstrate the reliable identification of single-cell subcompartments and offer new insights into cell-to-cell variability of nuclear subcompartments. Using scHi-C data from the human prefrontal cortex, scGhost identifies cell type-specific subcompartments that are strongly connected to cell type-specific gene expression, suggesting the functional implications of single-cell subcompartments. Overall, scGhost is an effective new method for single-cell 3D genome subcompartment annotation based on scHi-C data for a broad range of biological contexts.

Identifiers

PMID37292994
PMCPMC10245874

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.